Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations3678
Missing cells6712
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory698.1 B

Variable types

Categorical10
Text4
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
builtup_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with builtup_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_builtup_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%) Imbalance
facing has 1045 (28.4%) missing values Missing
super_builtup_area has 1802 (49.0%) missing values Missing
builtup_area has 1988 (54.1%) missing values Missing
carpet_area has 1806 (49.1%) missing values Missing
area is highly skewed (γ1 = 29.73502544) Skewed
builtup_area is highly skewed (γ1 = 40.70646407) Skewed
carpet_area is highly skewed (γ1 = 24.33323909) Skewed
floorNum has 129 (3.5%) zeros Zeros
facility_score has 462 (12.6%) zeros Zeros

Reproduction

Analysis started2025-05-17 19:00:18.257252
Analysis finished2025-05-17 19:00:26.785727
Duration8.53 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.7 KiB
flat
2819 
house
859 

Length

Max length5
Median length4
Mean length4.2335508
Min length4

Characters and Unicode

Total characters15571
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhouse
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Length

2025-05-18T00:30:26.849254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:26.900064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size294.0 KiB
2025-05-18T00:30:27.271438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.87245
Min length1

Characters and Unicode

Total characters62040
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowindependent
2nd rowthe close south
3rd rowsignature global city 63a
4th rowsaan verdante
5th rowpuri diplomatic greens
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 210
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.5%
heights 134
 
1.4%
Other values (783) 7500
77.5%
2025-05-18T00:30:27.683204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6715
 
10.8%
6006
 
9.7%
a 5862
 
9.4%
r 4173
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3720
 
6.0%
s 3474
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18396
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6715
 
10.8%
6006
 
9.7%
a 5862
 
9.4%
r 4173
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3720
 
6.0%
s 3474
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18396
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6715
 
10.8%
6006
 
9.7%
a 5862
 
9.4%
r 4173
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3720
 
6.0%
s 3474
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18396
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6715
 
10.8%
6006
 
9.7%
a 5862
 
9.4%
r 4173
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3720
 
6.0%
s 3474
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18396
29.7%

sector
Text

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
2025-05-18T00:30:27.853592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3183796
Min length3

Characters and Unicode

Total characters34273
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 25
2nd rowsector 50
3rd rowsector 63a
4th rowsector 95
5th rowsector 111
ValueCountFrequency (%)
sector 3449
46.7%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 99
 
1.3%
69 93
 
1.3%
90 88
 
1.2%
81 87
 
1.2%
109 86
 
1.2%
Other values (107) 2924
39.6%
2025-05-18T00:30:28.057503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 779
 
2.3%
Other values (21) 6208
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 779
 
2.3%
Other values (21) 6208
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 779
 
2.3%
Other values (21) 6208
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1076
 
3.1%
0 803
 
2.3%
8 779
 
2.3%
Other values (21) 6208
18.1%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5333543
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:28.115590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9802752
Coefficient of variation (CV)1.1764147
Kurtosis14.938219
Mean2.5333543
Median Absolute Deviation (MAD)0.72
Skewness3.2797205
Sum9274.61
Variance8.8820401
MonotonicityNot monotonic
2025-05-18T00:30:28.179700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 61
 
1.7%
1.3 57
 
1.5%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.1%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13891.022
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:28.246433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716
Q16818
median9020
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7060

Descriptive statistics

Standard deviation23207.11
Coefficient of variation (CV)1.6706554
Kurtosis186.97609
Mean13891.022
Median Absolute Deviation (MAD)2793
Skewness11.438643
Sum50855031
Variance5.3856996 × 108
MonotonicityNot monotonic
2025-05-18T00:30:28.317049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3510
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.0284
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:28.382622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11233
median1733
Q32300
95-th percentile4246
Maximum875000
Range874950
Interquartile range (IQR)1067

Descriptive statistics

Standard deviation23164.348
Coefficient of variation (CV)8.0208173
Kurtosis942.28725
Mean2888.0284
Median Absolute Deviation (MAD)533
Skewness29.735025
Sum10573072
Variance5.3658702 × 108
MonotonicityNot monotonic
2025-05-18T00:30:28.452873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
1950 43
 
1.2%
3240 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3268
88.9%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.3 KiB
2025-05-18T00:30:28.867371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.231648
Min length12

Characters and Unicode

Total characters199464
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowPlot area 150(125.42 sq.m.)
2nd rowSuper Built up area 2491(231.42 sq.m.)Built Up area: 2450 sq.ft. (227.61 sq.m.)Carpet area: 2250 sq.ft. (209.03 sq.m.)
3rd rowSuper Built up area 1081(100.43 sq.m.)
4th rowSuper Built up area 1965(182.55 sq.m.)Carpet area: 1935 sq.ft. (179.77 sq.m.)
5th rowSuper Built up area 3000(278.71 sq.m.)Carpet area: 2950 sq.ft. (274.06 sq.m.)
ValueCountFrequency (%)
area 5574
18.5%
sq.m 3656
12.1%
up 3021
 
10.0%
built 2317
 
7.7%
super 1876
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8701
28.9%
2025-05-18T00:30:29.197725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9207
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82362
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9207
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82362
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9207
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82362
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9207
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82362
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3599782
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:29.244134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8973801
Coefficient of variation (CV)0.56470012
Kurtosis18.218941
Mean3.3599782
Median Absolute Deviation (MAD)1
Skewness3.4857171
Sum12358
Variance3.6000513
MonotonicityNot monotonic
2025-05-18T00:30:29.297087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1497
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1497
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4244154
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:29.346700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9478158
Coefficient of variation (CV)0.56880241
Kurtosis17.548118
Mean3.4244154
Median Absolute Deviation (MAD)1
Skewness3.2493833
Sum12595
Variance3.7939863
MonotonicityNot monotonic
2025-05-18T00:30:29.398956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1078
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1078
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
3+
1173 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3189233
Min length1

Characters and Unicode

Total characters4851
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3+
3rd row2
4th row3+
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1173
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%

Length

2025-05-18T00:30:29.455573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:29.493678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2247
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2247
46.3%
+ 1173
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2247
46.3%
+ 1173
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2247
46.3%
+ 1173
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2247
46.3%
+ 1173
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7980322
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:29.547062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0116468
Coefficient of variation (CV)0.88432161
Kurtosis4.5176196
Mean6.7980322
Median Absolute Deviation (MAD)3
Skewness1.6940262
Sum24874
Variance36.139898
MonotonicityNot monotonic
2025-05-18T00:30:29.609080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 184
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 184
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size258.2 KiB
East
623 
North-East
623 
North
388 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8374478
Min length4

Characters and Unicode

Total characters18003
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowNorth-East
3rd rowNorth
4th rowNorth-East
5th rowEast

Common Values

ValueCountFrequency (%)
East 623
16.9%
North-East 623
16.9%
North 388
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2025-05-18T00:30:29.668594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:29.718796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
east 623
23.7%
north-east 623
23.7%
north 388
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3775
21.0%
s 2014
11.2%
o 1761
9.8%
h 1761
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1204
 
6.7%
r 1204
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3775
21.0%
s 2014
11.2%
o 1761
9.8%
h 1761
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1204
 
6.7%
r 1204
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3775
21.0%
s 2014
11.2%
o 1761
9.8%
h 1761
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1204
 
6.7%
r 1204
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3775
21.0%
s 2014
11.2%
o 1761
9.8%
h 1761
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1204
 
6.7%
r 1204
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.5 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
306 
Old Property
304 

Length

Max length18
Median length14
Mean length13.385536
Min length9

Characters and Unicode

Total characters49232
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOld Property
2nd rowModerately Old
3rd rowUnder Construction
4th rowNew Property
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 304
 
8.3%
Under Construction 266
 
7.2%

Length

2025-05-18T00:30:29.779901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:29.822741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.3%
property 897
12.7%
old 867
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8432
17.1%
l 4722
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2218
 
4.5%
Other values (15) 14069
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8432
17.1%
l 4722
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2218
 
4.5%
Other values (15) 14069
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8432
17.1%
l 4722
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2218
 
4.5%
Other values (15) 14069
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8432
17.1%
l 4722
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2308
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2218
 
4.5%
Other values (15) 14069
28.6%
Distinct3672
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size265.8 KiB
2025-05-18T00:30:30.009524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9980968
Min length7

Characters and Unicode

Total characters33095
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3666 ?
Unique (%)99.7%

Sample

1st rowR70162684
2nd rowH69433380
3rd rowG69165782
4th rowU70096330
5th rowS69742910
ValueCountFrequency (%)
m62958574 2
 
0.1%
c69153300 2
 
0.1%
k69091354 2
 
0.1%
w69369638 2
 
0.1%
g66828720 2
 
0.1%
i69118750 2
 
0.1%
g69165782 1
 
< 0.1%
u70096330 1
 
< 0.1%
f68728864 1
 
< 0.1%
i69037888 1
 
< 0.1%
Other values (3662) 3662
99.6%
2025-05-18T00:30:30.279521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 5562
16.8%
9 3587
10.8%
8 3241
9.8%
0 3068
9.3%
4 2781
8.4%
2 2716
8.2%
7 2441
7.4%
5 2103
 
6.4%
1 2021
 
6.1%
3 1897
 
5.7%
Other values (26) 3678
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 5562
16.8%
9 3587
10.8%
8 3241
9.8%
0 3068
9.3%
4 2781
8.4%
2 2716
8.2%
7 2441
7.4%
5 2103
 
6.4%
1 2021
 
6.1%
3 1897
 
5.7%
Other values (26) 3678
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 5562
16.8%
9 3587
10.8%
8 3241
9.8%
0 3068
9.3%
4 2781
8.4%
2 2716
8.2%
7 2441
7.4%
5 2103
 
6.4%
1 2021
 
6.1%
3 1897
 
5.7%
Other values (26) 3678
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 5562
16.8%
9 3587
10.8%
8 3241
9.8%
0 3068
9.3%
4 2781
8.4%
2 2716
8.2%
7 2441
7.4%
5 2103
 
6.4%
1 2021
 
6.1%
3 1897
 
5.7%
Other values (26) 3678
11.1%

super_builtup_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.1599
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:30.340616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.75
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.25

Descriptive statistics

Standard deviation763.97585
Coefficient of variation (CV)0.39683762
Kurtosis10.356525
Mean1925.1599
Median Absolute Deviation (MAD)372
Skewness1.8371922
Sum3611599.9
Variance583659.1
MonotonicityNot monotonic
2025-05-18T00:30:30.403638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1635
44.5%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

builtup_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct644
Distinct (%)38.1%
Missing1988
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2379.666
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:30.470892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.894
Coefficient of variation (CV)7.5400894
Kurtosis1667.8644
Mean2379.666
Median Absolute Deviation (MAD)650
Skewness40.706464
Sum4021635.5
Variance3.2194746 × 108
MonotonicityNot monotonic
2025-05-18T00:30:30.652492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1988
54.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8115.9806 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)39.2%
Missing1806
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:30.721142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2025-05-18T00:30:30.785466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1806
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2973 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Length

2025-05-18T00:30:30.843147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:30.878258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3022 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Length

2025-05-18T00:30:30.918967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:30.953965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2025-05-18T00:30:30.993861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:31.028266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2350 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Length

2025-05-18T00:30:31.071267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:31.104168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3273 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Length

2025-05-18T00:30:31.145217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:31.178217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2436 
2
1038 
1
 
204

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 204
 
5.5%

Length

2025-05-18T00:30:31.220911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T00:30:31.257916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 204
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 204
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 204
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 204
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 204
 
5.5%

facility_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.522567
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-05-18T00:30:31.317908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.055094
Coefficient of variation (CV)0.74179516
Kurtosis-0.88042318
Mean71.522567
Median Absolute Deviation (MAD)38
Skewness0.45856046
Sum263060
Variance2814.843
MonotonicityNot monotonic
2025-05-18T00:30:31.392913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
42 45
 
1.2%
37 45
 
1.2%
Other values (151) 2314
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-05-18T00:30:25.741348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.009239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.632881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.232296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.836140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.613993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.245989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.823991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.402552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.172565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.802225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.086442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.693887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.290331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.899144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.684989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.305604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.875982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.461298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.234032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.858773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.149131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.753120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.354865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.963780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.746699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.358697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.933720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.521941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.291995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.921545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.204942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.803906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.406619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.027660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.809464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.414127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.985623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.578420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.353321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.988545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.266093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.868891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.469600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.091678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.875346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.475273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.044040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.640283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.409594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:26.056067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.329974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.931492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.533241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.158074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.940998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.532791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.102241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.702624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.467397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:26.108293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.389099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.988396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.592230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.220681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.998993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.592889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.152913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.759342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.521122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:26.167333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.455734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.047928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.654882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.404702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.054744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.651218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.208638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.807408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.578141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:26.227109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.515476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.107753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.718382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.482817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.118427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.713279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.286506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.051039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.626769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:26.286201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:20.571472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.167882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:21.777376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:22.549582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.180428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:23.767254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:24.343267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.108582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T00:30:25.685395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-18T00:30:31.456517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuiltup_areacarpet_areafacility_scorefacingfloorNumfurnishing_typeotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_builtup_area
agePossession1.0000.0000.2740.1110.1290.0000.0000.2550.0920.1250.2140.1080.1870.1020.0560.3790.2870.1420.1400.086
area0.0001.0000.0110.6870.6240.8350.8010.2590.0220.1160.0430.0420.0370.7440.2070.0280.0150.0390.0180.948
balcony0.2740.0111.0000.2250.1750.0000.0260.2230.0150.0790.1780.0810.1970.1360.0330.2140.4400.1460.1820.306
bathroom0.1110.6870.2251.0000.8620.4640.5990.1790.044-0.0050.1940.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom0.1290.6240.1750.8621.0000.3800.5690.0570.032-0.1040.1660.0790.2910.6810.4170.5950.3170.2230.1540.800
builtup_area0.0000.8350.0000.4640.3801.0000.9690.2891.0000.0910.0900.0000.0000.6050.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.5990.5690.9691.0000.2390.0000.1590.0000.0160.0000.6130.1360.0000.0000.0000.0030.894
facility_score0.2550.2590.2230.1790.0570.2890.2391.0000.0650.2320.2380.1760.1890.2150.0540.3290.3470.2280.1830.222
facing0.0920.0220.0150.0440.0321.0000.0000.0651.0000.0000.0550.0000.0290.0210.0000.0940.0360.0350.0000.000
floorNum0.1250.1160.079-0.005-0.1040.0910.1590.2320.0001.0000.0260.0330.1030.001-0.1260.4850.0830.1120.0790.152
furnishing_type0.2140.0430.1780.1940.1660.0900.0000.2380.0550.0261.0000.0630.2130.1740.0220.0850.2650.1560.1370.131
others0.1080.0420.0810.0700.0790.0000.0160.1760.0000.0330.0631.0000.0330.0340.0360.0260.0000.1060.0310.084
pooja room0.1870.0370.1970.2860.2910.0000.0000.1890.0290.1030.2130.0331.0000.3340.0430.2520.2520.3050.3130.157
price0.1020.7440.1360.7200.6810.6050.6130.2150.0210.0010.1740.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sqft0.0560.2070.0330.4110.4170.1320.1360.0540.000-0.1260.0220.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.3790.0280.2140.4720.5950.0000.0000.3290.0940.4850.0850.0260.2520.5430.2011.0000.0650.2410.1281.000
servant room0.2870.0150.4400.5200.3170.0000.0000.3470.0360.0830.2650.0000.2520.3690.0440.0651.0000.1610.1850.584
store room0.1420.0390.1460.2440.2230.0000.0000.2280.0350.1120.1560.1060.3050.3030.0000.2410.1611.0000.2260.046
study room0.1400.0180.1820.1760.1540.0000.0030.1830.0000.0790.1370.0310.3130.2440.0300.1280.1850.2261.0000.120
super_builtup_area0.0860.9480.3060.8190.8000.9260.8940.2220.0000.1520.1310.0840.1570.7720.2871.0000.5840.0460.1201.000

Missing values

2025-05-18T00:30:26.392370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-18T00:30:26.507804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-18T00:30:26.706545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionproperty_idsuper_builtup_areabuiltup_areacarpet_areastudy roompooja roomstore roomservant roomothersfurnishing_typefacility_score
0houseindependentsector 254.3532222.01350.0Plot area 150(125.42 sq.m.)5433.0NorthOld PropertyR70162684NaN1350.0NaN00010049
1flatthe close southsector 503.1012444.02491.0Super Built up area 2491(231.42 sq.m.)Built Up area: 2450 sq.ft. (227.61 sq.m.)Carpet area: 2250 sq.ft. (209.03 sq.m.)343+4.0North-EastModerately OldH694333802491.02450.02250.0000102165
2flatsignature global city 63asector 63a1.6515263.01081.0Super Built up area 1081(100.43 sq.m.)2221.0NaNUnder ConstructionG691657821081.0NaNNaN0000000
3flatsaan verdantesector 952.0010178.01965.0Super Built up area 1965(182.55 sq.m.)Carpet area: 1935 sq.ft. (179.77 sq.m.)343+5.0NorthNew PropertyU700963301965.0NaN1935.000010049
4flatpuri diplomatic greenssector 1113.9013000.03000.0Super Built up area 3000(278.71 sq.m.)Carpet area: 2950 sq.ft. (274.06 sq.m.)453+6.0North-EastRelatively NewS697429103000.0NaN2950.0000101160
5flatsignature global city 63asector 63a1.4011336.01235.0Carpet area: 1235 (114.74 sq.m.)2223.0EastUnder ConstructionI69037888NaNNaN1235.00000100
6flatansal heights 86sector 861.055541.01895.0Super Built up area 1895(176.05 sq.m.)3339.0North-EastUnder ConstructionD265861241895.0NaNNaN0001000
7flatkamroon courtsector 432.7513750.02000.0Carpet area: 2000 (185.81 sq.m.)343+5.0North-EastModerately OldK69957846NaNNaN2000.000011249
8houseuppal southendsector 493.7525562.01467.0Built Up area: 163 (136.29 sq.m.)663+3.0EastModerately OldH64867042NaN163.0NaN10000226
9flatdlf regency parksector 282.3513224.01777.0Super Built up area 1777(165.09 sq.m.)33218.0North-EastOld PropertyT696216081777.0NaNNaN000100120
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionproperty_idsuper_builtup_areabuiltup_areacarpet_areastudy roompooja roomstore roomservant roomothersfurnishing_typefacility_score
3792flatbestech park view citysector 481.8010250.01756.0Carpet area: 1756 (163.14 sq.m.)3433.0North-WestModerately OldI64914368NaNNaN1756.010000276
3793houseindependent house sector 45sector 409.0038022.02367.0Plot area 2367(219.9 sq.m.)Built Up area: 9000 sq.ft. (836.13 sq.m.)Carpet area: 6000 sq.ft. (557.42 sq.m.)121234.0North-WestNew PropertyE69310182NaN9000.06000.000010271
3794houseindependentsector 30.808155.0981.0Plot area 109(91.14 sq.m.)5412.0NaNModerately OldJ55756986NaN981.0NaN0000000
3796flatdlf park placesector 544.8530180.01607.0Super Built up area 1983(184.23 sq.m.)Built Up area: 1785 sq.ft. (165.83 sq.m.)Carpet area: 1607 sq.ft. (149.3 sq.m.)343+6.0NorthModerately OldY697230181983.01785.01607.0000101158
3797flatsupertech aravillesector 790.805266.01519.0Carpet area: 1519 (141.12 sq.m.)22212.0NaNNew PropertyG68851842NaNNaN1519.010000049
3798flattulip violetsector 691.388966.01539.0Super Built up area 1538(142.88 sq.m.)33112.0WestRelatively NewB693550101538.0NaNNaN01000086
3799flatparas irenesector 70a1.309219.01410.0Super Built up area 1420(131.92 sq.m.)Carpet area: 1410 sq.ft. (130.99 sq.m.)22319.0NaNModerately OldD700271021420.0NaN1410.000000249
3800flatdlf the primussector 82a1.8510176.01818.0Super Built up area 1818(168.9 sq.m.)Carpet area: 1538 sq.ft. (142.88 sq.m.)33315.0EastModerately OldR692714061818.0NaN1538.000001038
3801flatchintamanisector 1031.578509.01845.0Built Up area: 1845 (171.41 sq.m.)Carpet area: 1350 sq.ft. (125.42 sq.m.)3331.0North-EastUndefinedK69908978NaN1845.01350.00000000
3802flatsmart world orchardsector 612.0012911.01549.0Carpet area: 1549 (143.91 sq.m.)3323.0NaNNew PropertyQ69870078NaNNaN1549.000001061